AI Engineer – Agent Reasoning & Multimodal Dialogue

Shopee; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Shopee is seeking an experienced researcher-engineer for the Engineering and Technology team in Singapore. You will design and implement core agent algorithms, including multi-turn dialogue planning, tool orchestration, and memory architectures using knowledge graphs to support personalization and session persistence.

Work involves emotion-aware dialogue modeling, LLM alignment and safety, multimodal reasoning, and deployment across GPU clusters.

Qualifications

• Hands-on research and engineering in conversational agents.
• Experience with knowledge-graph memory and persona-aware dialogue.
• Publications in top AI/NLP venues desired.
• Proficiency in Python, Bash; PyTorch and Hugging Face.

Responsibilities

• Design and develop core agent algorithms, including multi-turn dialogue planning.
• Build agent memory architectures using knowledge graphs.
• Develop emotion-aware dialogue modeling techniques.
• Design LLM alignment and safety strategies (SFT, DPO).
• Benchmark and deploy LLMs/VLMs on GPU clusters and cloud environments.

Skills

Python

PyTorch

HuggingFace

Knowledge graphs

Reinforcement learning

NLP

Research

Bash

Education

Master's degree in CS

Tools

AWS EC2

PostgreSQL

Neo4j

AWS S3

Job description

Department Engineering and TechnologyLevelExperienced (Individual Contributor)LocationSingaporeThe Engineering and Technology team is at the core of the Shopee platform development. The team is made up of a group of passionate engineers from all over the world, striving to build the best systems with the most suitable technologies. Our engineers do not merely solve problems at hand; We build foundations for a long-lasting future. We don't limit ourselves on what we can or can't do; we take matters into our own hands even if it means drilling down to the bottom layer of the computing platform. Shopee's hyper-growing business scale has transformed most \"innocent\" problems into huge technical challenges, and there is no better place to experience it first-hand if you love technologies as much as we do.Job Description:Design and develop core agent algorithms, including multi-turn dialogue planning, tool orchestration (retrieval, ranking, LLM synthesis), and adaptive task planning.Build and maintain agent memory architectures using knowledge graphs to support long-term consistency, personalization, and context retention across sessions.Develop emotion-aware dialogue modeling techniques to improve agent naturalness, consistency, and user engagement.Design and implement LLM alignment and safety strategies (e.g., SFT, DPO) to mitigate risks such as implicit persuasion or psychological manipulation in personalized generation.Build multimodal agent capabilities that integrate vision-language reasoning with dialogue planning for tasks such as tutoring or adaptive guidance.Benchmark and deploy LLMs/VLMs across GPU clusters and cloud environments (e.g., AWS); build reproducible evaluation pipelines to support model and architecture selection.Track frontier research in agent algorithms, contribute to publications, and represent findings at top-tier AI/NLP venues.Requirements:Master's degree or above in Computer Science, Natural Language Processing, Artificial Intelligence, or a related field.Minimum 3 years of hands-on research and engineering full-time working experience building conversational agents with knowledge-graph-based memory systems, persona-aware and emotion-aware dialogue modeling, and LLM alignment techniques (SFT and DPO) for safety and behavior control, combined with experience orchestrating agent pipelines involving retrieval, ranking, and tool use.First-author publication(s) at top-tier venues (ACL/AAAI/EMNLP/ICLR) on persona-driven dialogue generation and persona attribute extraction, particularly methods that improve dialogue consistency and personalization quality.Demonstrated experience building vision-language tutoring/dialogue agents that integrate multimodal reasoning with adaptive dialogue planning, applying reinforcement learning (e.g., Deep Q-Networks) to sequential decision-making problems, and developing end-to-end 3D reconstruction pipelines (segmentation, planar extraction, geometric reconstruction) from point cloud data.Good programming skills in Python and Bash; proficient in PyTorch and Hugging Face; familiar with LoRA/PEFT, prompt engineering, and model evaluation pipelines.Experience benchmarking and deploying LLMs/VLMs across GPU clusters and cloud platforms (e.g., AWS EC2); familiar with data systems such as PostgreSQL, Neo4j, and AWS S3.Good problem analysis and research skills; sustained curiosity in frontier AI/agent research; able to work independently and collaboratively across research and engineering teams.